EEGIFT: Group Independent Component Analysis for Event-Related EEG Data

Figure 1

Group ICA. In the group ICA model, we assume that the EEG is a linear mixture of temporally independent sources in each subject 𝑠(𝑡). The linear combination of sources is represented by the unknown mixing matrix 𝐴, and yields the ideal samples of brain activity u(t), and the signals recorded with the EEG amplifier (𝐸). Transformations (𝑇) during preprocessing contain filtering, epoching, artefact rejection, individual ICA for additional artefact reduction and so forth, altering the effective temporal sampling and dimensionality of the data 𝑦(𝑖). For each individual separately, the pre-processed single trial data are pre-whitened and reduced to 𝑅 via PCA. Group data is generated by concatenating individual principal components in the aggregate data set 𝐺. Temporal ICA is performed in this set, estimating aggregate components (𝐶). From the aggregate components, the individual data are reconstructed (see text for details).